Establishing reliable miRNA-cancer association network based on text-mining method

e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

MicroRNA-146b-5p suppresses cholangiocarcinoma cells by targeting TRAF6 and modulating p53 translocation

BACKGROUND

In view of the poor prognosis and high mortality of cholangiocarcinoma, there is a need for new therapeutic strategies. This study aims to reveal the biological function of miR-146b-5p in cholangiocarcinoma cell and its possible mechanism.

METHODS

The expression level and prognostic information on miR-146b-5p in cholangiocarcinoma were obtained in TCGA database. The biological function of miR-146b-5p on proliferation and vitality of cholangiocarcinoma cell HUCCT-1 was examined by EdU and MTT assay, and the apoptosis of HUCCT-1 cells transfected with miR-146b-5p mimic, mimic control, inhibitor, inhibitor control was detected by flow cytometry analysis. The western blot was done to evaluate the effect of miR-146b-5p targeting substrate and the expression of p53 in whole-cell protein and mitochondria fractions.

RESULTS

Our finding revealed that miR-146b-5p expression in patients with CHOL was lower than the normal group(p＜0.001). MiR-146b-5p expression was down-regulated in human cholangiocarcinoma HUCCT-1 and RBE cells compared to normal control HIBEC and other cancer cells. The miR-146b-5p mimic could inhibit HUCCT-1 cell proliferation (p＜0.05) and promote HUCCT-1 cell apoptosis significantly (p＜0.05). The results of western blot showed that miR-146b-5p mimic could directly target TRAF6 3'UTR region and up-regulate the expression of p53 in mitochondria and miR-146b-5p inhibitor could down-regulated the level of p53 in mitochondria.

CONCLUSION

MiR-146b-5p is a cholangiocarcinoma suppressor by inhibiting cell proliferation and promoting cell apoptosis with targeting TRAF6, possibly via modulating p53 translocation to mitochondria.

Comparisons of MicroRNA Set Enrichment Analysis Tools on Cancer De-regulated miRNAs from TCGA Expression Datasets

Background:

De-regulation of microRNAs (miRNAs) is closely related to many complex diseases, including cancers. In The Cancer Genome Atlas (TCGA), hundreds of differentially expressed miRNAs are stored for each type of cancer, which are hard to be intuitively interpreted. To date, several miRNA set enrichment tools have been tailored to predict the potential disease associations and functions of de-regulated miRNAs, including the miRNA Enrichment Analysis and Annotation tool (miEAA) and Tool for Annotations of human MiRNAs (TAM1.0 &TAM 2.0). However, independent benchmarking of these tools is warranted to assess their effectiveness and robustness, and the relationship between enrichment analysis results and the prognosis significance of cancers.

Methods:

Based on differentially expressed miRNAs from expression profiles in TCGA, we performed a series of tests and a comprehensive comparison of the enrichment analysis results of miEAA, TAM 1.0 and TAM 2.0. The work focused on the performance of the three tools, disease similarity based on miRNA-disease associations from the enrichment analysis results, the relationship between the overrepresented miRNAs from enrichment analysis results and the prognosis significance of cancers.

Results:

The main results show that TAM 2.0 is more likely to identify the regulatory disease’s functions of de-regulated miRNA; it is feasible to calculate disease similarity based on enrichment analysis results of TAM 2.0; and there is weak positive correlation between the occurrence frequency of miRNAs in the TAM 2.0 enrichment analysis results and the prognosis significance of the cancer miRNAs.

Conclusion:

Our comparison results not only provide a reference for biomedical researchers to choose appropriate miRNA set enrichment analysis tools to achieve their purpose but also demonstrate that the degree of overrepresentation of miRNAs could be a supplementary indicator of the disease similarity and the prognostic effect of cancer miRNAs.

Molecular subtypes of pancreatic cancer based on miRNA expression profiles have independent prognostic value

BACKGROUND AND AIM

Altered microRNAs (miRNA) expression, a typical feature of many cancers, is reportedly associated with prognosis according to several studies. Although numerous studies on miRNAs in pancreatic ductal adenocarcinoma have also attempted to identify prognostic biomarkers, more large-scale clinical studies are needed to establish the clinical significance of the results. Present study aimed to identify prognosis-related molecular subtypes of primary pancreas tumors using miRNA expression profiling.

METHODS

Expression profiles of 1733 miRNAs were obtained by using microarray analysis of 104 pancreatic tumors of Korean patients. To detect subgroups informative in predicting the patient's prognosis, we applied unsupervised clustering methods and then analyzed the association of the molecular subgroups with survival time. Then, we constructed a classifier to predict the subgroup using penalized regression models.

RESULTS

We have determined three pancreatic ductal adenocarcinoma tumor subtypes associated with prognosis based on miRNA expression profiles. These subtypes showed significantly different survival time for patients with the same clinical conditions. This demonstrates that our prognostic molecular subgroup has independent prognostic utility. The molecular subtypes can be predicted with a classifier of 19 miRNAs. Of the 19 signature miRNAs, miR-106b-star, miR-324-3p, and miR-615 were related to a p53 canonical pathway, and miR-324, miR-145-5p, miR-26b-5p, and miR-574-3p were related to a Cox-2 centered pathway.

CONCLUSIONS

Our prognostic molecular subtypes demonstrated that miRNA profiles could be used as prognostic markers. Additionally, we have constructed a classifier that may be used to determine the molecular subgroup of new patient sample data. Further studies are needed for validation.

microRNAs: role in leukemia and their computational perspective

MicroRNAs (miRNAs) belong to the family of noncoding RNAs (ncRNAs) and had gained importance due to its role in complex biochemical pathways. Changes in the expression of protein coding genes are the major cause of leukemia. Role of miRNAs as tumor suppressors has provided a new insight in the field of leukemia research. Particularly, the miRNAs mediated gene regulation involves the modulation of multiple mRNAs and cooperative action of different miRNAs to regulate a particular gene expression. This highly complex array of regulatory pathway network indicates the great possibility in analyzing and identifying novel findings. Owing to the conventional, slow experimental identification process of miRNAs and their targets, the last decade has witnessed the development of a large amount of computational approaches to deal with the complex interrelations present within biological systems. This article describes the various roles played by miRNAs in regulating leukemia and the role of computational approaches in exploring new possibilities.